Conversations that resolve, not just deflect
Most chatbots succeed at one metric and one metric only: they make the contact-volume report look better while customer satisfaction falls. We build conversational systems with a different definition of success — actual resolution of the customer's issue, end-to-end, including the booking, the payment, the status update, the escalation. The bot does the work, not the deflection.
What we deliver
Channels as a feature, not a project
One bot serving WhatsApp Business, the web widget, Telegram, Facebook Messenger, Microsoft Teams, voice IVR, and email — with a single intent model, a single knowledge base, and channel-specific rendering handled by the platform. Adding a new channel is a configuration change, not a re-implementation.
RAG-grounded knowledge
Answers grounded in your private knowledge base — product documents, policies, FAQs, regulatory guidance — with retrieval that returns source paragraphs as citations. No hallucinations on factual questions, with an answerability classifier that escalates rather than confabulates when the knowledge base does not cover the question.
Action-taking agents
The bot calls your APIs to actually do things: book the appointment, update the address, raise the dispute, restart the modem, transfer the funds within limits, raise the ticket with full context. Authentication is delegated to your identity provider, audit logs flow to your SIEM, and the bot has only the permissions you grant it.
Multilingual including the hard languages
Twelve languages out of the box including Arabic with Gulf and Levantine dialects, English, French, Swahili, Hausa, Yoruba, Hindi, Urdu, Bengali, Tamil, Bahasa, and Portuguese. We train on your historical conversation logs so the bot speaks like your customers, not like a US translation engine.
Human handoff with full context
When the bot escalates, the agent inherits the full conversation transcript, the customer's identified intent, the actions already taken, the customer's identity from your IDP, and the suggested next-best action. Zendesk, Freshdesk, Salesforce Service Cloud, ServiceNow, or your custom CRM — pre-built connectors for the common ones, REST for the rest.
Conversation intelligence
Intent heatmaps, deflection and resolution rates, CSAT correlated to conversation patterns, prompt-injection and abuse detection, and unresolved-question mining that feeds your knowledge-base team. The platform tells you which content to write next based on what customers actually asked.
WhatsApp banking bot
How a query actually flows.
A real trace through the sovereign stack. Six stages, ~1.4 seconds end-to-end, zero packets leaving your perimeter.
How we deliver
Top-intent mapping
We analyse three to six months of historical contact logs to identify the top twenty intents by volume, the top ten by handling cost, and the top five by customer pain. The intersection becomes the launch backlog. We will not let you start with a bot that answers questions nobody is asking.
Build and integrate
Two to four weeks to implement the launch intents, connect the knowledge base, integrate the action APIs, and configure the channel surfaces. Every action is end-to-end tested against your sandbox systems before any customer sees it.
Shadow on real traffic
We run the bot in shadow mode against live customer conversations for two weeks, scoring intent classification, answer accuracy, and would-be-resolution rate without affecting actual customers. Misclassifications and gaps get fixed before launch.
Controlled launch
Deploy to a single channel and a subset of customers, monitor every conversation, and ramp up over four to six weeks. War-room support from MindMap for go-live and the first month of hypercare.
Continuous expansion
Sprint cadence adds intents, languages, channels, and integrations. The platform improves automatically from production traffic — misclassifications you label become training data, unanswered questions feed the knowledge-base team, and the resolution rate trends up quarter on quarter.
Chatbots & NLP across every sector
The stack we build on
NLP engine
Channels
Knowledge and retrieval
Integrations and analytics
"We launched on WhatsApp on a Friday with eight intents. Six months later it handles sixty-seven percent of our inbound volume across four channels and twenty-three intents, in English and Swahili, and our NPS has gone up eighteen points. The bot does not deflect — it resolves."— Head of Digital, Tier-1 East African Bank
How we work together
Common questions
What is the difference between ChatNext and a custom build?+
ChatNext is our production-tested platform with four years of deployment across banking, telecoms, and retail — fifty-plus enterprise installations, three million-plus monthly conversations, twelve languages. A custom build on Rasa or LangChain makes sense when your requirements are highly specific or you need full source-code ownership. Most clients start with ChatNext and add custom modules where they need them — a hybrid pattern that gives speed without sacrificing control.
How do you handle languages that are poorly supported by mainstream NLP?+
We have built and fine-tuned models for African languages — Swahili, Hausa, Yoruba, Amharic — and dialectal Gulf and Levantine Arabic on training data drawn from our clients' actual conversation logs. The base capability is multilingual BERT or a Llama derivative, the fine-tunes are bespoke per language. For a new language we typically need twenty to fifty thousand labelled examples to reach production accuracy, which we draw from your historical contacts.
Can the bot actually take actions in our core systems?+
Yes — this is the design point. Our bots are agentic: they call your APIs to update records, raise tickets, book appointments, transfer funds within policy limits, and send confirmations. Authentication is delegated to your identity provider so the bot only has the customer's permissions; audit logs flow to your SIEM; rate limits and circuit breakers protect your downstream systems. Deflection without resolution is just an obstacle course — we will not build that.
How do you measure success beyond containment?+
Containment is the vanity metric. We measure first-contact resolution — did the customer's actual problem get solved without escalation — alongside post-bot CSAT, action-completion time, and downstream contact rate. Bots that artificially contain volume by frustrating customers into hanging up show up as high containment with high downstream contact and falling CSAT; the platform surfaces this pattern and flags it for tuning.
What happens with prompt injection and abuse?+
Layered defences. Input classifiers detect jailbreak attempts and obvious abuse before the prompt reaches the LLM. Output validators check responses against policy before delivery. Rate limits and behavioural anomaly detection catch automated abuse. A red-team test suite — built collaboratively with your security team — runs against every release. We document the residual risk in your CISO's language, not in marketing speak.
How long does deployment really take?+
On ChatNext, forty-eight hours to a working bot on a single channel with three to five intents — useful for proof. Two to four weeks to a launch-ready bot with ten-to-fifteen intents, integrations, and tested escalation. Eight to twelve weeks to a scaled deployment across multiple channels and languages. Custom builds add four to eight weeks for the foundation. Anyone promising you a production-grade conversational AI in a week is selling a demo.
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